{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'add_newdocs' from 'numpy' (D:\\Progrram Files\\anaconda\\lib\\site-packages\\numpy\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-198b122dbfe5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Progrram Files\\anaconda\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    124\u001b[0m \u001b[1;31m# cbook must import matplotlib only within function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    125\u001b[0m \u001b[1;31m# definitions, so it is safe to import from it here.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 126\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    127\u001b[0m from matplotlib.cbook import (\n\u001b[0;32m    128\u001b[0m     _backports, mplDeprecation, dedent, get_label, sanitize_sequence)\n",
      "\u001b[1;32mD:\\Progrram Files\\anaconda\\lib\\site-packages\\matplotlib\\cbook\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     32\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mweakref\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mref\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mWeakKeyDictionary\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     35\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Progrram Files\\anaconda\\lib\\site-packages\\numpy\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mloader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mpackages\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 142\u001b[1;33m     \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0madd_newdocs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    143\u001b[0m     __all__ = ['add_newdocs',\n\u001b[0;32m    144\u001b[0m                \u001b[1;34m'ModuleDeprecationWarning'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'add_newdocs' from 'numpy' (D:\\Progrram Files\\anaconda\\lib\\site-packages\\numpy\\__init__.py)"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7e0f6a0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0,math.pi * 2, 0.005)\n",
    "y = np.sin(x)\n",
    "plt.xlabel(\"angle\")\n",
    "plt.ylabel(\"sine\")\n",
    "plt.title(\"sine wave\")\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pylab\n",
    "for i in range(100):\n",
    "    x = pylab.linspace(-3,3,i)\n",
    "    y = x ** 2\n",
    "    pylab.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
